SOTAVerified

Question Answering

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

Papers

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Showing 9151–9200 of 10817 papers

TitleStatusHype
Overcoming the vanishing gradient problem in plain recurrent networks—0
An Interpretable Reasoning Network for Multi-Relation Question AnsweringCode0
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience—0
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge GraphsCode0
MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection—0
Denotation Extraction for Interactive Learning in Dialogue SystemsCode0
Biomedical Question Answering via Weighted Neural Network Passage Retrieval—0
Analysis of Wikipedia-based Corpora for Question Answering—0
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching ModelCode0
VnCoreNLP: A Vietnamese Natural Language Processing ToolkitCode0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text—0
Using OpenWordnet-PT for Question Answering on Legal Domain—0
Training RNNs as Fast as CNNsCode2
Topic-Based Question Generation—0
Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions—0
Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks—0
A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Adversarial reading networks for machine comprehension—0
Questionable Answers in Question Answering Research: Reproducibility and Variability of Published Results—0
A Syntactic Approach to Domain-Specific Automatic Question Generation—0
Interpretable Counting for Visual Question Answering—0
TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang—0
The NarrativeQA Reading Comprehension ChallengeCode0
Visual Explanations from Hadamard Product in Multimodal Deep Networks—0
Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval—0
Relation Extraction : A SurveyCode0
AI2-THOR: An Interactive 3D Environment for Visual AICode1
Stochastic Answer Networks for Machine Reading ComprehensionCode0
Contextualized Word Representations for Reading ComprehensionCode0
A Deep Network Model for Paraphrase Detection in Short Text Messages—0
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks—0
Learning by Asking Questions—0
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks—0
Forewords—0
Question Retrieval with Distributed Representations and Participant Reputation in Community Question Answering—0
Multimodal Learning and Reasoning for Visual Question Answering—0
JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System—0
IJCNLP-2017 Task 5: Multi-choice Question Answering in Examinations—0
ALS at IJCNLP-2017 Task 5: Answer Localization System for Multi-Choice Question Answering in Exams—0
ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task—0
MappSent at IJCNLP-2017 Task 5: A Textual Similarity Approach Applied to Multi-choice Question Answering in Examinations—0
YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English—0
YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations—0
YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model—0
Chinese Answer Extraction Based on POS Tree and Genetic Algorithm—0
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question AnsweringCode0
Embodied Question AnsweringCode0
Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end—0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified